Due to the significance of transportation planning, traffic management, and dispatch optimization, predicting passenger origin–destination has emerged as a crucial requirement for intelligent transportation systems management. In this study, we present a model designed to forecast the origin and destination of travels within a specified time window. To derive meaningful travel flows, we employ K-means clustering in a four-dimensional space with a maximum cluster size constraint for origin and destination zones. Given the large number of clusters, we utilize non-negative matrix factorization to reduce the number of travel clusters. Furthermore, we implement a stacked recurrent neural network model to predict the travel count in each cluster. A comparison of our results with existing models reveals that our proposed model achieves a 5–7% lower mean absolute percentage error (MAPE) for 1-h time windows and a 14% lower MAPE for 30-min time windows.


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    Title :

    Proposing a model for predicting passenger origin–destination in online taxi-hailing systems


    Subtitle :

    Predicting passenger origin–destination P. Golshanrad et al.


    Additional title:

    Public Transp


    Contributors:

    Published in:

    Public Transport ; 17 , 1 ; 121-151


    Publication date :

    2025-03-01


    Size :

    31 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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